Update README.md

README.md

 # BERT-pytorch
-BERT BERT BERT
+
+Repeted by myself, without pre-train.
+
+# How to use
+
+Bash code(preparation)
+
+    sudo apt-get install ipython3
+    sudo apt-get install pip
+    sudo apt-get install git
+    git clone https://github.com/stevezhangz/BERT-pytorch.git
+    cd BERT-pytorch
+    pip install -r requirements.txt 
+    
+I prepare two demos for model training(poem and a conversation)
+run train_demo.py to train
+  
+    ipython3 train_demo.py
+
+except that, you have to learn about how to run it on your dataset
+
+(1)first use "general_transform_text2list" in data_process.py to transform txt or json file to list which defined as "[s1,s2,s3,s4.....]"
+(2)then use "generate_vocab_normalway" in data_process.py to transform list file to "sentences, id_sentence, idx2word, word2idx, vocab_size"
+(3)Last but not least, use "creat_batch" in data_process.py to transform "sentences, id_sentence, idx2word, word2idx, vocab_size" to a batch.
+(4)finally using dataloder in pytorch to load data.
+for example:
+
+    #json2list=general_transform_text2list("data/demo.txt",type="txt")
+    json2list=general_transform_text2list("data/chinese-poetry/chuci/chuci.json",type="json",args=['content'])
+    data=json2list.getdata()
+    # transform list to token
+    list2token=generate_vocab_normalway(data,map_dir="words_info.json")
+    sentences,token_list,idx2word,word2idx,vocab_size=list2token.transform()
+    batch = creat_batch(batch_size,max_pred,maxlen,vocab_size,word2idx,token_list,sentences)
+    input_ids, segment_ids, masked_tokens, masked_pos, isNext = zip(*batch)
+    input_ids, segment_ids, masked_tokens, masked_pos, isNext = \
+        torch.LongTensor(input_ids), torch.LongTensor(segment_ids), torch.LongTensor(masked_tokens), \
+        torch.LongTensor(masked_pos), torch.LongTensor(isNext)
+    loader = Data.DataLoader(Text_file(input_ids, segment_ids, masked_tokens, masked_pos, isNext), batch_size, True)
+    model=Bert(n_layers=n_layers,
+                     vocab_size=vocab_size,
+                     emb_size=d_model,
+                     max_len=maxlen,
+                     seg_size=n_segments,
+                     dff=d_ff,
+                     dk=d_k,
+                     dv=d_v,
+                     n_head=n_heads,
+                     n_class=2,
+                    )
+    if use_gpu:
+        with torch.cuda.device(device) as device:
+            model.to(device)
+            criterion = nn.CrossEntropyLoss()
+            optimizer = optim.Adadelta(model.parameters(), lr=lr)
+            model.Train(epoches=epoches,
+                        train_data_loader=loader,
+                        optimizer=optimizer,
+                        criterion=criterion,
+                        save_dir=weight_dir,
+                        save_freq=100,
+                        load_dir="checkpoint/checkpoint_199.pth",
+                        use_gpu=use_gpu,
+                        device=device
+                        )
+
+
+# How to config
+Modify super parameters directly in “Config.cfg”
+
+# Pretrain
+Because of time, I can't spend time to train the model. You are welcome to use my model for training and contribute pre train weight to this project
+
+# About me
+author={
+  E-maile:stevezhangz@163.com
+}
+
+# Acknowledgement
+Acknowledgement for the open-source [poem dataset](https://github.com/chinese-poetry/chinese-poetry)
+
+
+